Optimized hybrid YOLOu-Quasi-ProtoPNet for insulators classification

To ensure the electrical power supply, inspections are frequently performed in the power grid. Nowadays, several inspections are conducted considering the use of aerial images since the grids might be in places that are difficult to access. The classification of the insulators' conditions recorded in inspections through computer vision is challenging, as object identification methods can have low performance because they are typically pre-trained for a generalized task. Here, a hybrid method called YOLOu-Quasi-ProtoPNet is proposed for the detection and classification of failed insulators. This model is trained from scratch, using a personalized ultra-large version of YOLOv5 for insulator detection and the optimized Quasi-ProtoPNet model for classification. For the optimization of the Quasi-ProtoPNet structure, the backbones VGG-16, VGG-19, ResNet-34, ResNet-152, DenseNet-121, and DenseNet-161 are evaluated. The F1-score of 0.95165 was achieved using the proposed approach (based on DenseNet-161) which outperforms models of the same class such as the Semi-ProtoPNet, Ps-ProtoPNet, Gen-ProtoPNet, NP-ProtoPNet, and the standard ProtoPNet for the classification task

Generalized multivariate analysis of variance - A unified framework for signal processing in correlated noise

Generalized multivariate analysis of variance (GMANOVA) and related reduced-rank regression are general statistical models that comprise versions of regression, canonical correlation, and profile analyses as well as analysis of variance (ANOVA) and covariance in univariate and multivariate settings. It is a powerful and, yet, not very well-known tool. We develop a unified framework for explaining, analyzing, and extending signal processing methods based on GMANOVA. We show the applicability of this framework to a number of detection and estimation problems in signal processing and communications and provide new and simple ways to derive numerous existing algorithms. Many of the methods were originally derived from scratch , without knowledge of their close relationship with the GMANOVA model. We explicitly show this relationship and present new insights and guidelines for generalizing these methods. Our results could inspire applications of the general framework of GMANOVA to new problems in signal processing. We present such an application to flaw detection in nondestructive evaluation (NDE) of materials. A promising area for future growth is image processing